A Queuing Approach to Parking: Modeling, Verification, and Prediction

Abstract

We present a queuing model of parking dynamics and a model-based prediction method to provide realtime probabilistic forecasts of future parking occupancy. The queuing model has non-homogeneous arrival rate and time-varying service time distribution. All statistical assumptions of the model are verified using data from 29 truck parking locations, each with between 55 and 299 parking spots. For each location and each spot the data specifies the arrival and departure times of a truck, for 16 months of operation. The modeling framework presented in this paper provides empirical support for queuing models adopted in many theoretical studies and policy designs. We discuss how our framework can be used to study parking problems in different environments. Based on the queuing model, we propose two prediction methods, a microscopic method and a macroscopic method, that provide a real-time probabilistic forecast of parking occupancy for an arbitrary forecast horizon. These model-based methods convert a probabilistic forecast problem into a parameter estimation problem that can be tackled using classical estimation methods such as regressions or pure machine learning algorithms. We characterize a lower bound for an arbitrary realtime prediction algorithm. We evaluate the performance of these methods using the truck data comparing the outcomes of their implementations with other model-based and model-free methods proposed in the literature.

Enabling Factors and Durations Data Analytics for Dynamic Freight Parking Limits

“…With regard to occupancy forecast using durations, the literature refers to two main approaches for parking occupancy prediction (23). On the one hand, there are predictions based on model structures for parking processes, that is, queuing models, where future parking occupations are expected values computed with stochastic arrival and departure processes from a parking facility (15).…”

“…Since data availability has represented a constraint for accurate estimations of cruising and parking violations, queuing models for parking have opted for assuming unlimited capacity in systems M(t)=G(t)=' (23). Summed up to data scarcity, this choice is backed by a mature research field on queuing models for unlimited capacity, unlikely underdeveloped queuing systems with scarce capacity in which waiting times are longer than service times.…”

Online and Adaptive Parking Availability Mapping: An Uncertainty-Aware Active Sensing Approach for Connected Vehicles

“…For example, in [18], a multivariate autoregressive model is proposed where temporal and spatial correlations are taken into account to compute the probability of parking availability at the vehicle estimated arrival time; this system exploits real-time and historical data, where the latter are used to learn the parameters of the model and the former are used to carry out an accurate up-to-date prediction. Finally, queuing models are a popular and established method to describe the behavior of the vehicles-parking slots interaction and are employed in [19] to provide probabilistic estimation of parking availability, leveraging on learning techniques to attain the prediction model parameters.…”